import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=7 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=18 ,  lowerCAmelCase__=30 ,  lowerCAmelCase__=4_00 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  lowerCAmelCase__=False ,  )	->   int:

							'''simple docstring'''


							__lowercase					     =				size if size is not None else {'''height''': 20, '''width''': 20}
							__lowercase					     =				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				num_channels
							__lowercase					     =				image_size
							__lowercase					     =				min_resolution
							__lowercase					     =				max_resolution
							__lowercase					     =				do_resize
							__lowercase					     =				size
							__lowercase					     =				do_center_crop
							__lowercase					     =				crop_size
							__lowercase					     =				do_normalize
							__lowercase					     =				image_mean
							__lowercase					     =				image_std
							__lowercase					     =				do_reduce_labels





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				load_dataset('''hf-internal-testing/fixtures_ade20k'''				,     split='''test'''				)

				__lowercase					     =				Image.open(dataset[0]['''file''']				)
				__lowercase					     =				Image.open(dataset[1]['''file''']				)

				return image, map





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				load_dataset('''hf-internal-testing/fixtures_ade20k'''				,     split='''test'''				)

				__lowercase					     =				Image.open(ds[0]['''file''']				)
				__lowercase					     =				Image.open(ds[1]['''file''']				)
				__lowercase					     =				Image.open(ds[2]['''file''']				)
				__lowercase					     =				Image.open(ds[3]['''file''']				)

				return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class   _UpperCamelCase (		_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Dict     					=		BeitImageProcessor if is_vision_available() else None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				BeitImageProcessingTester(self		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_resize'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''size'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_center_crop'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''center_crop'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_normalize'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_mean'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_std'''		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class.from_dict(self.image_processor_dict		)
							self.assertEqual(image_processor.size ,  {'''height''': 20, '''width''': 20}		)
							self.assertEqual(image_processor.crop_size ,  {'''height''': 18, '''width''': 18}		)
							self.assertEqual(image_processor.do_reduce_labels ,  lowerCAmelCase__		)

							__lowercase					     =				self.image_processing_class.from_dict(
							    self.image_processor_dict ,  size=42 ,  crop_size=84 ,  reduce_labels=lowerCAmelCase__		)
							self.assertEqual(image_processor.size ,  {'''height''': 42, '''width''': 42}		)
							self.assertEqual(image_processor.crop_size ,  {'''height''': 84, '''width''': 84}		)
							self.assertEqual(image_processor.do_reduce_labels ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							pass





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  Image.Image		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__ ,  numpify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  np.ndarray		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__ ,  torchify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  torch.Tensor		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__ ,  torchify=lowerCAmelCase__		)
							__lowercase					     =				[]
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  torch.Tensor		)
											maps.append(torch.zeros(image.shape[-2:]		).long()		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  maps[0] ,  return_tensors='''pt'''		)
							self.assertEqual(
							    encoding['''pixel_values'''].shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(
							    encoding['''labels'''].shape ,  (
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(encoding['''labels'''].dtype ,  torch.long		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  lowerCAmelCase__ ,  return_tensors='''pt'''		)
							self.assertEqual(
							    encoding['''pixel_values'''].shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(
							    encoding['''labels'''].shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(encoding['''labels'''].dtype ,  torch.long		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

							# Test not batched input (PIL images)
							__lowercase     ,	__lowercase					     =				prepare_semantic_single_inputs()

							__lowercase					     =				image_processing(lowerCAmelCase__ ,  lowerCAmelCase__ ,  return_tensors='''pt'''		)
							self.assertEqual(
							    encoding['''pixel_values'''].shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(
							    encoding['''labels'''].shape ,  (
							        1,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(encoding['''labels'''].dtype ,  torch.long		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)

							# Test batched input (PIL images)
							__lowercase     ,	__lowercase					     =				prepare_semantic_batch_inputs()

							__lowercase					     =				image_processing(lowerCAmelCase__ ,  lowerCAmelCase__ ,  return_tensors='''pt'''		)
							self.assertEqual(
							    encoding['''pixel_values'''].shape ,  (
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(
							    encoding['''labels'''].shape ,  (
							        2,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
							self.assertEqual(encoding['''labels'''].dtype ,  torch.long		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							__lowercase     ,	__lowercase					     =				prepare_semantic_single_inputs()
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  lowerCAmelCase__ ,  return_tensors='''pt'''		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 1_50		)

							__lowercase					     =				True
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  lowerCAmelCase__ ,  return_tensors='''pt'''		)
							self.assertTrue(encoding['''labels'''].min().item() >= 0		)
							self.assertTrue(encoding['''labels'''].max().item() <= 2_55		)
def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				len(lowercase				)
				__lowercase					     =				len(lowercase				)
				__lowercase					     =				[[False for _ in range(m + 1				)] for _ in range(n + 1				)]
				__lowercase					     =				True
				for i in range(lowercase				):
								for j in range(m + 1				):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				__lowercase					     =				True
																if a[i].islower():
																				__lowercase					     =				True
				return dp[n][m]


if __name__ == "__main__":
		import doctest

		doctest.testmod()